<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>silk.backbones.loftr.linear_attention API documentation</title>
<meta name="description" content="Linear Transformer proposed in &#34;Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention&#34;
Modified from: …" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.loftr.linear_attention</code></h1>
</header>
<section id="section-intro">
<p>Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention"
Modified from: <a href="https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py">https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py</a></p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

&#34;&#34;&#34;
Linear Transformer proposed in &#34;Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention&#34;
Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py
&#34;&#34;&#34;

import torch
from torch.nn import Module, Dropout


def elu_feature_map(x):
    return torch.nn.functional.elu(x) + 1


class LinearAttention(Module):
    def __init__(self, eps=1e-6):
        super().__init__()
        self.feature_map = elu_feature_map
        self.eps = eps

    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
        &#34;&#34;&#34;Multi-Head linear attention proposed in &#34;Transformers are RNNs&#34;
        Args:
            queries: [N, L, H, D]
            keys: [N, S, H, D]
            values: [N, S, H, D]
            q_mask: [N, L]
            kv_mask: [N, S]
        Returns:
            queried_values: (N, L, H, D)
        &#34;&#34;&#34;
        Q = self.feature_map(queries)
        K = self.feature_map(keys)

        # set padded position to zero
        if q_mask is not None:
            Q = Q * q_mask[:, :, None, None]
        if kv_mask is not None:
            K = K * kv_mask[:, :, None, None]
            values = values * kv_mask[:, :, None, None]

        v_length = values.size(1)
        values = values / v_length  # prevent fp16 overflow
        KV = torch.einsum(&#34;nshd,nshv-&gt;nhdv&#34;, K, values)  # (S,D)&#39; @ S,V
        Z = 1 / (torch.einsum(&#34;nlhd,nhd-&gt;nlh&#34;, Q, K.sum(dim=1)) + self.eps)
        queried_values = torch.einsum(&#34;nlhd,nhdv,nlh-&gt;nlhv&#34;, Q, KV, Z) * v_length

        return queried_values.contiguous()


class FullAttention(Module):
    def __init__(self, use_dropout=False, attention_dropout=0.1):
        super().__init__()
        self.use_dropout = use_dropout
        self.dropout = Dropout(attention_dropout)

    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
        &#34;&#34;&#34;Multi-head scaled dot-product attention, a.k.a full attention.
        Args:
            queries: [N, L, H, D]
            keys: [N, S, H, D]
            values: [N, S, H, D]
            q_mask: [N, L]
            kv_mask: [N, S]
        Returns:
            queried_values: (N, L, H, D)
        &#34;&#34;&#34;

        # Compute the unnormalized attention and apply the masks
        QK = torch.einsum(&#34;nlhd,nshd-&gt;nlsh&#34;, queries, keys)
        if kv_mask is not None:
            QK.masked_fill_(
                ~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float(&#34;-inf&#34;)
            )

        # Compute the attention and the weighted average
        softmax_temp = 1.0 / queries.size(3) ** 0.5  # sqrt(D)
        A = torch.softmax(softmax_temp * QK, dim=2)
        if self.use_dropout:
            A = self.dropout(A)

        queried_values = torch.einsum(&#34;nlsh,nshd-&gt;nlhd&#34;, A, values)

        return queried_values.contiguous()</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.backbones.loftr.linear_attention.elu_feature_map"><code class="name flex">
<span>def <span class="ident">elu_feature_map</span></span>(<span>x)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def elu_feature_map(x):
    return torch.nn.functional.elu(x) + 1</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.loftr.linear_attention.FullAttention"><code class="flex name class">
<span>class <span class="ident">FullAttention</span></span>
<span>(</span><span>use_dropout=False, attention_dropout=0.1)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class FullAttention(Module):
    def __init__(self, use_dropout=False, attention_dropout=0.1):
        super().__init__()
        self.use_dropout = use_dropout
        self.dropout = Dropout(attention_dropout)

    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
        &#34;&#34;&#34;Multi-head scaled dot-product attention, a.k.a full attention.
        Args:
            queries: [N, L, H, D]
            keys: [N, S, H, D]
            values: [N, S, H, D]
            q_mask: [N, L]
            kv_mask: [N, S]
        Returns:
            queried_values: (N, L, H, D)
        &#34;&#34;&#34;

        # Compute the unnormalized attention and apply the masks
        QK = torch.einsum(&#34;nlhd,nshd-&gt;nlsh&#34;, queries, keys)
        if kv_mask is not None:
            QK.masked_fill_(
                ~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float(&#34;-inf&#34;)
            )

        # Compute the attention and the weighted average
        softmax_temp = 1.0 / queries.size(3) ** 0.5  # sqrt(D)
        A = torch.softmax(softmax_temp * QK, dim=2)
        if self.use_dropout:
            A = self.dropout(A)

        queried_values = torch.einsum(&#34;nlsh,nshd-&gt;nlhd&#34;, A, values)

        return queried_values.contiguous()</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.linear_attention.FullAttention.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.linear_attention.FullAttention.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.linear_attention.FullAttention.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, queries, keys, values, q_mask=None, kv_mask=None) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Multi-head scaled dot-product attention, a.k.a full attention.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>queries</code></strong></dt>
<dd>[N, L, H, D]</dd>
<dt><strong><code>keys</code></strong></dt>
<dd>[N, S, H, D]</dd>
<dt><strong><code>values</code></strong></dt>
<dd>[N, S, H, D]</dd>
<dt><strong><code>q_mask</code></strong></dt>
<dd>[N, L]</dd>
<dt><strong><code>kv_mask</code></strong></dt>
<dd>[N, S]</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>queried_values</code></dt>
<dd>(N, L, H, D)</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
    &#34;&#34;&#34;Multi-head scaled dot-product attention, a.k.a full attention.
    Args:
        queries: [N, L, H, D]
        keys: [N, S, H, D]
        values: [N, S, H, D]
        q_mask: [N, L]
        kv_mask: [N, S]
    Returns:
        queried_values: (N, L, H, D)
    &#34;&#34;&#34;

    # Compute the unnormalized attention and apply the masks
    QK = torch.einsum(&#34;nlhd,nshd-&gt;nlsh&#34;, queries, keys)
    if kv_mask is not None:
        QK.masked_fill_(
            ~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]), float(&#34;-inf&#34;)
        )

    # Compute the attention and the weighted average
    softmax_temp = 1.0 / queries.size(3) ** 0.5  # sqrt(D)
    A = torch.softmax(softmax_temp * QK, dim=2)
    if self.use_dropout:
        A = self.dropout(A)

    queried_values = torch.einsum(&#34;nlsh,nshd-&gt;nlhd&#34;, A, values)

    return queried_values.contiguous()</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.loftr.linear_attention.LinearAttention"><code class="flex name class">
<span>class <span class="ident">LinearAttention</span></span>
<span>(</span><span>eps=1e-06)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LinearAttention(Module):
    def __init__(self, eps=1e-6):
        super().__init__()
        self.feature_map = elu_feature_map
        self.eps = eps

    def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
        &#34;&#34;&#34;Multi-Head linear attention proposed in &#34;Transformers are RNNs&#34;
        Args:
            queries: [N, L, H, D]
            keys: [N, S, H, D]
            values: [N, S, H, D]
            q_mask: [N, L]
            kv_mask: [N, S]
        Returns:
            queried_values: (N, L, H, D)
        &#34;&#34;&#34;
        Q = self.feature_map(queries)
        K = self.feature_map(keys)

        # set padded position to zero
        if q_mask is not None:
            Q = Q * q_mask[:, :, None, None]
        if kv_mask is not None:
            K = K * kv_mask[:, :, None, None]
            values = values * kv_mask[:, :, None, None]

        v_length = values.size(1)
        values = values / v_length  # prevent fp16 overflow
        KV = torch.einsum(&#34;nshd,nshv-&gt;nhdv&#34;, K, values)  # (S,D)&#39; @ S,V
        Z = 1 / (torch.einsum(&#34;nlhd,nhd-&gt;nlh&#34;, Q, K.sum(dim=1)) + self.eps)
        queried_values = torch.einsum(&#34;nlhd,nhdv,nlh-&gt;nlhv&#34;, Q, KV, Z) * v_length

        return queried_values.contiguous()</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.linear_attention.LinearAttention.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.linear_attention.LinearAttention.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.linear_attention.LinearAttention.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, queries, keys, values, q_mask=None, kv_mask=None) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Multi-Head linear attention proposed in "Transformers are RNNs"</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>queries</code></strong></dt>
<dd>[N, L, H, D]</dd>
<dt><strong><code>keys</code></strong></dt>
<dd>[N, S, H, D]</dd>
<dt><strong><code>values</code></strong></dt>
<dd>[N, S, H, D]</dd>
<dt><strong><code>q_mask</code></strong></dt>
<dd>[N, L]</dd>
<dt><strong><code>kv_mask</code></strong></dt>
<dd>[N, S]</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>queried_values</code></dt>
<dd>(N, L, H, D)</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, queries, keys, values, q_mask=None, kv_mask=None):
    &#34;&#34;&#34;Multi-Head linear attention proposed in &#34;Transformers are RNNs&#34;
    Args:
        queries: [N, L, H, D]
        keys: [N, S, H, D]
        values: [N, S, H, D]
        q_mask: [N, L]
        kv_mask: [N, S]
    Returns:
        queried_values: (N, L, H, D)
    &#34;&#34;&#34;
    Q = self.feature_map(queries)
    K = self.feature_map(keys)

    # set padded position to zero
    if q_mask is not None:
        Q = Q * q_mask[:, :, None, None]
    if kv_mask is not None:
        K = K * kv_mask[:, :, None, None]
        values = values * kv_mask[:, :, None, None]

    v_length = values.size(1)
    values = values / v_length  # prevent fp16 overflow
    KV = torch.einsum(&#34;nshd,nshv-&gt;nhdv&#34;, K, values)  # (S,D)&#39; @ S,V
    Z = 1 / (torch.einsum(&#34;nlhd,nhd-&gt;nlh&#34;, Q, K.sum(dim=1)) + self.eps)
    queried_values = torch.einsum(&#34;nlhd,nhdv,nlh-&gt;nlhv&#34;, Q, KV, Z) * v_length

    return queried_values.contiguous()</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.loftr" href="index.html">silk.backbones.loftr</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.backbones.loftr.linear_attention.elu_feature_map" href="#silk.backbones.loftr.linear_attention.elu_feature_map">elu_feature_map</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.loftr.linear_attention.FullAttention" href="#silk.backbones.loftr.linear_attention.FullAttention">FullAttention</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.linear_attention.FullAttention.dump_patches" href="#silk.backbones.loftr.linear_attention.FullAttention.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.linear_attention.FullAttention.forward" href="#silk.backbones.loftr.linear_attention.FullAttention.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.linear_attention.FullAttention.training" href="#silk.backbones.loftr.linear_attention.FullAttention.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.loftr.linear_attention.LinearAttention" href="#silk.backbones.loftr.linear_attention.LinearAttention">LinearAttention</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.linear_attention.LinearAttention.dump_patches" href="#silk.backbones.loftr.linear_attention.LinearAttention.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.linear_attention.LinearAttention.forward" href="#silk.backbones.loftr.linear_attention.LinearAttention.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.linear_attention.LinearAttention.training" href="#silk.backbones.loftr.linear_attention.LinearAttention.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
</footer>
</body>
</html>